Automated techniques exist for identifying items that a consumer might enjoy in view of other items the consumer has previously indicated he or she enjoys. Some such techniques compare attributes of items the consumer previously indicated he or she enjoys with attributes of other items to identify items that the consumer might enjoy. Thus, for example, if the consumer enjoys “Dubliners” by James Joyce, “Ulysses” by James Joyce might be identified as another item the consumer may enjoy because both “Dubliners” and “Ulysses” have a common attribute (the author, James Joyce).
Other automated techniques utilize collaborative methods to identify items that the consumer might enjoy. For example, consumers who enjoyed “The Da Vinci Code” by Mark Brown might indicate that they also enjoyed “The Catcher in the Rye” by J. D. Salinger. Accordingly, if the consumer indicates that he or she enjoys “The Da Vinci Code,” “The Catcher in the Rye” would be identified as another item the consumer would enjoy because other consumers who enjoyed “The Da Vinci Code” indicated that they enjoyed “The Catcher in the Rye.”
One problem with these techniques is that they neglect the context of the attributes or information used to identify items that the consumer might enjoy. For example, consumers who enjoyed “The Da Vinci Code” and who indicated that they also enjoyed “The Catcher in the Rye” might only have enjoyed “The Catcher in the Rye” because they read “Dubliners,” which is written in a similar stream-of-consciousness style, immediately before “The Catcher in the Rye.” If so, then a recommendation such as “If you like ‘The Da Vinci Code,’ then you'll also like ‘The Catcher in the Rye’” may not be helpful to the consumer.
This problem also arises where the items are musical compositions. For example, radio station listeners may enjoy the song “House of the Rising Sun” by The Animals after the song “Stairway to Heaven” by Led Zeppelin, but not after the song “Aqualung” by Jethro Tull. However, existing automated techniques are only able to determine, for example, that radio station listeners who like “Stairway to Heaven” and “Aqualung” also like “House of the Rising Sun.” Accordingly, existing automated techniques cannot, for example, help create radio station playlists that account for listener preferences as to the context of a particular musical composition. Accordingly, there exists a need for methods and systems for using contextual information to generate and modify playlists that do account for such listener preferences.